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#Reinforcement Learning

Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization

Intermediate

Mizanur Rahman, Mohammed Saidul Islam et al.Jan 8arXiv

This paper teaches a model to turn a question about a table into both a short answer and a clear, correct chart.

#Text-to-Visualization#Reinforcement Learning#GRPO

MiMo-V2-Flash Technical Report

Intermediate

Xiaomi LLM-Core Team, : et al.Jan 6arXiv

MiMo-V2-Flash is a giant but efficient language model that uses a team-of-experts design to think well while staying fast.

#Mixture-of-Experts#Sliding Window Attention#Global Attention

Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling

Beginner

Falcon LLM Team, Iheb Chaabane et al.Jan 5arXiv

Falcon-H1R is a small (7B) AI model that thinks really well without needing giant computers.

#Falcon-H1R#Hybrid Transformer-Mamba#Chain-of-Thought

VAR RL Done Right: Tackling Asynchronous Policy Conflicts in Visual Autoregressive Generation

Intermediate

Shikun Sun, Liao Qu et al.Jan 5arXiv

Visual Autoregressive (VAR) models draw whole grids of image tokens at once across multiple scales, which makes standard reinforcement learning (RL) unstable.

#Visual Autoregressive (VAR)#Reinforcement Learning#GRPO

NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation

Intermediate

Huichao Zhang, Liao Qu et al.Jan 5arXiv

NextFlow is a single, decoder-only Transformer that can read and write both text and images in one continuous sequence.

#Next-Scale Prediction#Autoregressive Transformer#Dual-Codebook Tokenization

MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics

Intermediate

Zhuofan Shi, Hubao A et al.Jan 5arXiv

MDAgent2 is a special helper built from large language models (LLMs) that can both answer questions about molecular dynamics and write runnable LAMMPS simulation code.

#Molecular Dynamics#LAMMPS#Code Generation

K-EXAONE Technical Report

Intermediate

Eunbi Choi, Kibong Choi et al.Jan 5arXiv

K-EXAONE is a super-sized language model that speaks six languages and can read very long documents (up to 256,000 tokens) without forgetting important details.

#Mixture-of-Experts#Hybrid Attention#Sliding Window Attention

CPPO: Contrastive Perception for Vision Language Policy Optimization

Intermediate

Ahmad Rezaei, Mohsen Gholami et al.Jan 1arXiv

CPPO is a new way to fine‑tune vision‑language models so they see pictures more accurately before they start to reason.

#CPPO#Contrastive Perception Loss#Vision-Language Models

MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning

Intermediate

Jiawei Chen, Xintian Shen et al.Dec 29arXiv

MindWatcher is a smart AI agent that can think step by step and decide when to use tools like web search, image zooming, and a code calculator to solve tough, multi-step problems.

#Tool-Integrated Reasoning#Interleaved Thinking#Multimodal Chain-of-Thought

NVIDIA Nemotron 3: Efficient and Open Intelligence

Intermediate

NVIDIA, : et al.Dec 24arXiv

Nemotron 3 is a new family of open AI models (Nano, Super, Ultra) built to think better while running faster and cheaper.

#Nemotron 3#Mixture-of-Experts#LatentMoE

Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

Intermediate

NVIDIA, : et al.Dec 23arXiv

Nemotron 3 Nano is a new open-source language model that mixes two brain styles (Mamba and Transformer) and adds a team of special experts (MoE) so it thinks better while running much faster.

#Mixture-of-Experts#Mamba-2#Transformer

SpatialTree: How Spatial Abilities Branch Out in MLLMs

Intermediate

Yuxi Xiao, Longfei Li et al.Dec 23arXiv

SpatialTree is a new, four-level "ability tree" that tests how multimodal AI models (that see and read) handle space: from basic seeing to acting in the world.

#Spatial Intelligence#Multimodal Large Language Models#Hierarchical Benchmark

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